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A Nomogram for Predicting Overall Survival of Patients With Primary Spinal Cord Glioblastoma. | LitMetric

AI Article Synopsis

  • The study focuses on creating a prognostic nomogram for primary spinal cord glioblastoma (PSCGBM), a rare and aggressive cancer, to help predict patient survival.
  • Clinical data was collected from 132 PSCGBM patients; factors like age, tumor size, and treatment type were identified as key predictors of survival.
  • The developed nomogram demonstrated strong accuracy in predicting 1-year, 1.5-year, and 2-year overall survival rates, which could assist healthcare professionals in tailoring treatment plans for these patients.

Article Abstract

Objective: Primary spinal cord glioblastoma (PSCGBM) is a rare malignancy with a poor prognosis. To date, no prognostic nomogram for this rare disease was established. Hence, we aimed to develop a nomogram to predict overall survival (OS) of PSCGBM.

Methods: Clinical data of patients with PSCGBM was retrospectively collected from the neurosurgery department of Soochow University Affiliated Second Hospital and the Surveillance Epidemiology and End Results database. Information including age, sex, race, tumor extension, extent of resection, adjuvant treatment, marital status, income, year of diagnosis and months from diagnosis to treatment were recorded. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors for PSCGBM. A nomogram was constructed to predict 1-year, 1.5-year, and 2-year OS of PSCGBM.

Results: A total of 132 patients were included. The 1-year, 1.5-year, and 2-year OS were 45.5%, 29.5%, and 18.9%, respectively. Four variables: age groups, tumor extension, extent of resection, and adjuvant therapy, were identified as independent prognostic factors. The nomogram showed robust discrimination with a C-index value for the prediction of 1-year OS, 1.5-year OS, and 2-year of 0.71 (95% confidence interval [CI], 0.61-0.70), 0.72 (95% CI, 0.62-0.70), and 0.70 (95% CI, 0.61-0.70), respectively. The calibration curves exhibited high consistencies between the predicted and observed survival probability in this cohort.

Conclusion: We have developed and internally validated a nomogram for predicting the survival outcome of PSCGBM for the first time. The nomogram has the potential to assist clinicians in making individualized predictions of survival outcome of PSCGBM.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11224756PMC
http://dx.doi.org/10.14245/ns.2448082.041DOI Listing

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